It is well known that industrial plants produce not only vast amounts of energy but also emissions, like nitrogen oxides (NOx) and carbon oxides (CO), that can be harmful to humans and the environment. Thus, high efforts are made to reduce these pollutants. Hence, it is essential to monitor emissions from industrial plants. Moreover, depending on the size of an industrial plant, and applicable regulations, the continuous monitoring of emission levels (primarily NOx) is legal requirement for some industrial combustion processes.
Continuous monitoring of plant emissions may be done by either Automated Emission Monitoring Systems (AMS), which is a direct continuous method of emissions measurement, or by a Predicted Emissions Monitoring System (PEMS), which uses characteristic process parameters to calculate (predict) emission levels. Of the two methods, PEMS has a significantly lower operating cost and complexity of operation. However, PEMS models typically require significant ‘training’ and calibration on site, and only tend to be applicable to a specific plant within calibrated operating/ambient conditions (see below). Such a system is for example described in EP 1 864 193 B1.
Because PEMS models are typically produced by third parties who may not have the same detailed product knowledge available compared to that of an Original Equipment Manufacturer (OEM), current PEMS models typically rely heavily on on-site ‘training’ (i.e. neural network-type setups) and calibration, and the model varies from site to site even for the same plant configuration. While a basic model may exist, and have some fundamental combustion characteristics included, the model is essentially an empirically-based relationship between process parameter and emission levels. Such models often require regular ‘recalibration’ following an engine's normal degradation over time. The accuracy of these models at ambient and operational conditions beyond their calibration range are questionable.